Principal Architect - Data

Drive the future of enterprise data at one of the world's most influential financial institutions — where your architectural decisions determine how data is produced, governed, and consumed at scale, and where strategy without execution is not enough. As a Principal Architect at JPMorganChase within Foundational Services, you will establish a practical, enterprise-grade data architecture discipline that enables product teams to consistently produce, manage, integrate, and consume data across the organization. This role is not limited to producing strategy, selecting governance tools, or reviewing designs after the fact — you are accountable for making data architecture executable. You will translate standards into usable engineering patterns, help teams adopt them in live delivery, and resolve cross-product data decisions that individual teams cannot settle alone. Foundational Services is a critical platform organization within JPMorganChase, responsible for the shared capabilities, integrations, and data infrastructure that product teams depend on. You will partner closely with a Principal Data Modeler and a broad set of architects, engineers, and product teams — setting direction, making cross-product decisions, securing executive alignment, and removing delivery blockers while ensuring detailed modeling is connected to delivery practices and enterprise decisions. Job responsibilities Define the target-state data architecture for Foundational Services, establishing clear boundaries between systems of record, analytical data stores, caches, and derived data products, and prioritizing the highest-risk domains, systems, and integrations rather than attempting to model everything at once Develop a prioritized data architecture roadmap that moves teams from inconsistent, after-the-fact data design to a model-first delivery discipline, with a conceptual and logical model covering priority domains and their critical data elements delivered within the first year Establish reusable engineering patterns for producing, sharing, moving, storing, and consuming data — including APIs, events, streaming, batch processing, and replication — and prove those patterns in real product delivery, improving them based on what works Define and govern standards for data contracts, schema evolution, metadata management, lineage, data quality, retention, lifecycle management, and ownership, producing execution-ready assets — reference architectures, templates, decision trees, and implementation checklists — that allow teams to adopt patterns without designing them from scratch Lead enterprise data architecture decisions, including canonical data definitions, systems-of-record strategy, cross-product data semantics, and integration design, resolving conflicts that individual teams cannot settle independently and securing named ownership for critical data elements and cross-product interfaces Require material schema and integration changes to have an updated model, named owner, and approved data contract before implementation, and work with the Principal Data Modeler and product architects to keep logical and physical models synchronized Establish a lightweight architecture review and exception process that supports delivery while protecting critical data decisions — building a small number of clear rules, giving teams usable ways to follow them, and managing exceptions deliberately Coach architects, engineers, product teams, and data owners on approved patterns and standards, and build executive alignment on the architectural decisions needed to prevent local shortcuts from becoming enterprise-wide problems Assess logical-model and physical-schema alignment for priority applications, lead remediation of existing model and schema drift — including reverse-engineering where required — and report adoption, exceptions, model coverage, ownership gaps, and major integrity risks to leadership Required qualifications, capabilities, and skills Formal training or certification on architecture concepts and 10+ years applied experience Extensive experience in data architecture, enterprise architecture, software architecture, or a comparable technical leadership role within a large, complex organization Deep working knowledge of conceptual, logical, and physical data modeling, relational design, identifiers, integrity constraints, schema evolution, and data contracts Experience designing and governing data integration patterns across APIs, event-driven architectures, streaming platforms, batch processing, replication, and analytical environments Demonstrated ability to establish common data semantics, ownership models, and canonical definitions across multiple products, systems, and business domains — including resolving conflicting definitions across teams Proven ability to turn architectural standards into implementation guidance, reusable patterns, reference designs, and delivery controls that engineering teams actually use Experience working with legacy systems where models, schemas, integrations, and ownership are incomplete or inconsistent, including reverse-engineering and remediation Ability to lead through influence across engineering, architecture, product, operations, risk, and senior stakeholders, and to enter ambiguous environments, make sound decisions with incomplete information, and revise direction when delivery evidence demands it Strong written and verbal communication skills, with the ability to explain a technical trade-off to engineers and articulate a delivery, control, or operational risk to executives Preferred qualifications, capabilities, and skills Experience in financial services or another highly regulated, high-availability environment Experience defining systems-of-record strategy, operational versus analytical data boundaries, and data-product ownership models Experience with enterprise streaming, event-driven architecture, data platforms, metadata and lineage tooling, or data catalog capabilities Experience introducing model-first design, schema governance, or data-contract practices into an established organization Experience leading or mentoring data architects, data modelers, software architects, or engineering teams Familiarity with data classification, privacy, retention, access control, auditability, and resiliency requirements This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorganChase’s review of criminal conviction history, including pretrial diversions or program entries.